TABLE 2.

Combined-approach study methodologies

Reference	Subject	Modeling	Bioinformatics	Integration
Examples of GRN modeling of EMT
Khan et al., 2017 [35]	E2F-mediated EMT in cancer	Boolean network simulations and in silico perturbations	E2F family interactions curated from TRANSFAC, STRING, HPRD, MiRTarBase; >98% validated by domain experts	GRNs for breast and bladder cancer constructed by ranking global network motifs by (1) topological properties, (2) agreement with gene expression in target datasets, (3) agreement with KEGG cancer pathways
Udyavar et al., 2017 [36]; Wooten et al., 2019 [91]	EMT in SCLC	Developed BooleaBayes, a Boolean network modeling framework that can also estimate probabilities	Clustering, weighted gene coexpression network analysis (WGCNA), and GRN inference with ARACNE filtered with TF-target databases, literature review	Boolean network modeling to predict multiple SCLC subtypes and subtype-specific master regulators
Kohar and Lu, 2018 [42]	EMT in SCC	Ensemble ODE-based simulations with RACIPE and stochastic noise	Incorporated GRNs from a previous study on Epcam+ and Epcam− cells using RNA-seq and ATAC-seq	Combination of manually curated core EMT network with SCC-specific networks from previous genome-wide study
Ramirez et al., 2020 [66]	EMT in cancer	Ensemble ODE-based simulations with RACIPE	SCENIC used to infer GRNs for each dataset and identify conserved and context-specific interactions	Iterative GRN construction and SCENIC parameter optimization by comparing simulated and experimental data
Sha et al., 2020 [123]	EMT in cancer and embryogenesis	Stochastic ODE-based multiscale simulation of a core EMT circuit	QuanTC is developed, which identifies clusters, marker genes, and transition genes from scRNA-seq data	QuanTC applied to multiple EMT datasets to validate the behaviors predicted by the model
Examples of GRN modeling of other processes
Moignard et al., 2015 [120]	Mouse hematopoiesis	Boolean network modeling	Single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR) on ~40 genes; Developed single-cell network synthesis (SCNS) toolkit to construct Boolean networks from discretized expression data	Using SCNS, a GRN was constructed to identify key regulators, which were later validated experimentally
Dunn et al., 2014 [122]; Dunn et al., 2019 [121]	mESCs	Abstract Boolean network (ABN) modeling—ensemble Boolean networks based on experimental constraints	Initial coexpression network from microarray and RNA-seq data, qRT-PCR and clonal assays with siRNA to test model predictions	Iteratively refined a meta-model of multiple Boolean networks by experimentally validating model predictions